IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing - Image and Video Coding beyond Standards
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Real-Time Face Detection
International Journal of Computer Vision
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Novel Eye Location Algorithm based on Radial Symmetry Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new method of feature fusion and its application in image recognition
Pattern Recognition
Fusion of PCA-based and LDA-based similarity measures for face verification
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Precise localization of eye centers with multiple cues
Multimedia Tools and Applications
Hi-index | 0.00 |
Automatic face recognition is a challenge task, especially working in practical uncontrolled environments. Over the past two decades, numerous innovative ideas and effective processing approaches had been proposed and developed, e.g. various normalization techniques, intrinsic feature extractions and representation schemes, machine learning methods and recognition mechanisms etc. Those approaches based on different principles had been shown possessing varying degrees of effectiveness in different aspects. It is expected that the techniques of information fusion with integrating the advantages of existing methods will boost the recognition performance. This paper deals with developing effective approaches for face recognition using information fusion techniques based on integrating multiple cues. The multiple stage integrating techniques dedicated to localization of landmark points and pose estimation were presented. The precise data of localization of landmarks and pose estimation provide the essential geometry basics for further processing. A face recognition classifier scheme with integration of multiple feature representation and multiple block region scores is also proposed. The experiment results show that the proposed approach can reduce equal error rate EER significantly, compared with using single feature and single block representations. The proposed approach had been shown possessing the best performance in participating MCFR2011 competition.